Prediction models can be crucial statistical tools to help companies make informed decisions. These models are basically ways of predicting events that will happen in the future based on patterns of the past through statistics. Prediction models allow those who are anticipating or considering change to become informed of the most likely result of said change. It also allows companies to more accurately make budgeting decisions as a predictive model can anticipate the potential revenues that the a product or app will continue to make.
For an example of the possible application of prediction models within the decision making of a company consider a predictive model which factors in the ad revenue that an app has made, how many users view the app and how long they tend to use it. This predictive model could favor in the growth (or lack thereof) of app use and how much revenue is gained from that to determine how much revenues the app will make in the next year. This can help determine the financial viability of an app and whether or not a change should be made to the app or revenue source.
Predictive models are set up in a series of steps. First relevant predictors are assessed and decided upon, then from these relative predictors data is collected, this data is in turn used to formulate a statistical model. This model is revised and updated upon new data collected after the predictive model was created. The math that these models will utilize can vary greatly depending on the complexity required. Sometimes simple linear equations are all that is needed but in more complex models entire neural networks can be employed and utilized.
The question many business may have is whether or not they can trust the statistics. Whether what this statistical model says about potential revenue effects of actions is accurate and factual. Should, in other words, the numbers be trusted. While it’s true that, to a certain extent, the past can’t always be used to understand the future, it is important to note that these models have been tried and tested in a vast array of fields, from email carriers, to health care providers, to hospitals, to technology firms and overall these models have shown themselves to be remarkably effective. Using statistics to understand the future, especially in relationship to human behavior and future revenues has been incredibly successful and without a doubt is relied upon by many companies.
Predictive modeling is a widely used tool that helps businesses everywhere justify and inform their decisions with reliable and accurate information

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